ATAAT is an adaptive adversarial tuning method that enables effective, stealthy backdoor attacks on VLA models by dynamically selecting gradient decoupling strategies based on attacker capabilities.
Your agent can defend itself against backdoor attacks
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.RO 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Backdoor attacks aligned with JSON command formats in LLM robot controllers achieve 83% attack success rate while preserving over 93% clean accuracy and sub-second latency.
citing papers explorer
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ATAAT: Adaptive Threat-Aware Adversarial Tuning Framework against Backdoor Attacks on Vision-Language-Action Models
ATAAT is an adaptive adversarial tuning method that enables effective, stealthy backdoor attacks on VLA models by dynamically selecting gradient decoupling strategies based on attacker capabilities.
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From Prompt to Physical Action: Structured Backdoor Attacks on LLM-Mediated Robotic Control Systems
Backdoor attacks aligned with JSON command formats in LLM robot controllers achieve 83% attack success rate while preserving over 93% clean accuracy and sub-second latency.